databases, ontologies and text mining session introduction part 2 carole goble, university of...
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Databases, Ontologies and Text mining
Session IntroductionPart 2
Carole Goble, University of Manchester, UKDietrich Rebholz-Schuhmann, EBI, UK
Philip Bourne, SDSC/UCSD, [email protected]
UniP
rot
The Gene O
ntology
Ontologies
DatabasesApplications
and Mining
Bioinformatics
LocusLink
Text
min
ing
Knowledge mining
Resources in Bioinformatics
UniP
rotDatabases
Bioinformatics
LocusLink
Resources in Bioinformatics
What perspective do I bring?
Preface
• A review of the state and needs of the field from the perspective of a user of biological databases….
… the p53 core domain structure consists of a ß sandwich that serves as a scaffold for two large loops and a loop-sheet- helix motif ... ----Science Vol.265, p346
1TSR
Corresponding structure from the PDB
?Oops!
ß sandwich? Where?Large loop? Which one??
Loop-sheet-helix???
Preface
• A review of the state and needs of the field from the perspective of a developer of biological databases….
What are the current biological databases and what does this tell
us?
Large Growth in the Number of Biological Databases
NAR Database Issue
0
100
200
300
400
500
600
1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
Nu
mb
er o
f E
ntr
ies
Resources are Becoming More Diverse Database Types
Nucleotide Sequence
RNA Sequence
Protein Sequence
Structure
Genome (non-human)
Pathways
Genome (human)
Disease
Gene Expression Other
NAR 2004 – Division by Resource Type
NAR 2004 – A Closer Look
Database Types
Nucleotide Sequence
RNA Sequence
Protein Sequence
Structure
Genome (non-human)
Pathways
Genome (human)
Disease
Gene Expression Other
• Genome scale databases have proliferated
• Traditional sequence databases are now a small part
• Databases around new specific data types are emerging
• Pathway and disease orientated databases are emerging
The Future - ISMB04 Poster Distribution
Nucleotide
Sequence
RNA Sequence
Protein Sequence
Structure
Genome (non-
human)
Pathways
Genome (human)Disease
Gene Expression
Other
Database Types
Nucleotide Sequence
RNA Sequence
Protein Sequence
Structure
Genome (non-human)
Pathways
Genome (human)
Disease
Gene Expression Other
ISMB04
What Does ISMB04 Tell Us About New Biological Databases?
• Microarray data resources are hot• Genotypic – phenotypic resources are
emerging• Surprisingly pathway resources are not
growing fast • Disease and species based resources are
increasing – notably plants• Human genome related resources are
increasing
What About Data in These Databases?
Data are Becoming More Plentiful and More Complex
Note: Redundancy at 30% Sequence Identity
Data are Becoming More Redundant
So the amount and complexity of data are increasing across biological scales – what are the challenges?
A Major Challenge
12:00
We suffer from the “high noon syndrome”
Those who can gain and contribute most to biological databasesare frequently NOT the users
We need to lower the cost:benefit ratio
How Do We Lower this Barrier?
• Better support of complex data types e.g., networks, images, graphs
• Associated optimized query languages
• Associated ontologies
• Better handling of uncertainty and inconsistency
• More and automated data curation
• Large scale data integration
How Do We Lower this Barrier?
• Better support of complex data types e.g., networks, images, graphs
• Associated optimized query languages
• Associated ontologies
• Better handling of uncertainty and inconsistency
• More and automated data curation
• Large scale data integration
How Do We Lower this Barrier?
• Support of data provenance
• Support for rapid data and associated schema evolution
• Support for temporal data
• Better integration of data and methods
• Usability engineering
How Do We Lower this Barrier?
• Support of data provenance
• Support for rapid data and associated schema evolution
• Support for temporal data
• Better integration of data and methods
• Usability engineering
We need more work in these other areas
A Note on Data Provenance
Further Reading
• Jagadish and Olken (2003) Omics 7(1) 131-137. Data Management for Life Sciences Research http://www.lbl.gov/~olken/wmdbio
• Maojo and Kulikowski (2003) J. of AMIA 515-522. Bioinformatics and Medical Informatics – Collaborations on the Road to Genomic Medicine?
GeneXPress: A Visualization and Statistical Analysis Tool for Gene Expression and
Sequence DataSegal, Kaushal, Yelensky, Pham, Regev, Koller,
Friedman
DataQuery &Analysis
BiologicalResults
Curation
Usability Integration
• Assign biological meaning to gene expression data through post-processing and visualization
Filtering Erroneous Protein AnnotationWieser, Kretschmann and Apweiler
DataQuery &Analysis
BiologicalResults
Curation
Usability Integration
• Automated detection of annotation errors using a decision tree approach based upon the C4.5 data mining algorithm
Selecting Biomedical Data Sources According to User Preferences
Cohen-Boulakia, Lair, Stransky, Graziani, Radvanyi, Barillot and Froidevaux
DataQuery &Analysis
BiologicalResults
Curation
Usability Integration
• Understand the characteristics of biological data
• Present a selection of resources relevant to a user query
• Framework for the multiple parametric analysis of cancer
Integration of Biological Data from Web Resources: Management of Multiple Answers through Metadata Retrieval
Devignes, Smail
DataQuery &Analysis
BiologicalResults
Curation
Usability Integration
• Same question – different answers from different resources – How can this be understood?
• Semantic integration based on domain ontologies
Critically-based Task Composition in Distributed Bioinformatics Systems
Karasavvas, Baldock, Burger
Data Query &Analysis
BiologicalResults
Curation
Usability Integration
• Task composition in workflow systems requires decision support
• Provision of data providing providence information provides that support
ENJOY !!